Data Exploration of pollutants plus weather in Berlin
# importing our data
data = data.import()Read 5 items
Read 3 items
Read 3 items
Read 5 items
Read 3 items
Read 3 items
Read 4 items
Read 4 items
Read 18 items
Read 6 items
Read 8 items
Read 6 items
Read 18 items
Read 6 items
Read 8 items
Read 6 items
Read 18 items
Read 6 items
Read 8 items
Read 6 items
Read 9 items
Read 3 items
Read 2 items
Read 6 items
Read 13 items
Read 4 items
Read 7 items
Read 4 items
Read 3 items
Read 2 items
Read 2 items
the condition has length > 1 and only the first element will be usedhead(data)Overview
summary(data) dtPosix dt dt_iso city_id temp temp_min temp_max
Min. :2017-01-01 00:00:00 Min. :1.483e+09 2017-03-18 06:00:00 +0000 UTC: 3 Min. :2950159 Min. :263.1 Min. :261.1 Min. :263.7
1st Qu.:2017-04-01 10:45:00 1st Qu.:1.491e+09 2017-03-30 01:00:00 +0000 UTC: 3 1st Qu.:2950159 1st Qu.:277.1 1st Qu.:277.1 1st Qu.:277.4
Median :2017-07-03 15:30:00 Median :1.499e+09 2017-03-30 02:00:00 +0000 UTC: 3 Median :2950159 Median :283.1 Median :283.1 Median :283.1
Mean :2017-07-03 03:05:03 Mean :1.499e+09 2017-03-30 03:00:00 +0000 UTC: 3 Mean :2950159 Mean :283.4 Mean :283.0 Mean :283.7
3rd Qu.:2017-10-04 02:15:00 3rd Qu.:1.507e+09 2017-03-30 04:00:00 +0000 UTC: 3 3rd Qu.:2950159 3rd Qu.:289.1 3rd Qu.:289.1 3rd Qu.:289.1
Max. :2017-12-31 23:00:00 Max. :1.515e+09 (Other) :9104 Max. :2950159 Max. :305.1 Max. :305.1 Max. :305.1
NA's :37 NA's : 37 NA's :37 NA's :37 NA's :37 NA's :37
pressure humidity wind_speed wind_deg rain_3h clouds_all weather_id weather_main weather_description
Min. : 980 Min. : 14.00 Min. : 0.00 Min. : 0.0 Min. :0.118 Min. : 0.00 Min. :200.0 Clouds :3434 Sky is Clear :2972
1st Qu.:1010 1st Qu.: 67.00 1st Qu.: 2.00 1st Qu.:120.0 1st Qu.:0.150 1st Qu.: 0.00 1st Qu.:701.0 Clear :2973 broken clouds :2380
Median :1016 Median : 81.00 Median : 3.00 Median :230.0 Median :0.380 Median : 75.00 Median :800.0 Rain :1195 light rain : 803
Mean :1015 Mean : 77.17 Mean : 3.43 Mean :197.5 Mean :0.672 Mean : 45.13 Mean :728.3 Mist : 598 mist : 598
3rd Qu.:1021 3rd Qu.: 93.00 3rd Qu.: 5.00 3rd Qu.:270.0 3rd Qu.:0.889 3rd Qu.: 75.00 3rd Qu.:803.0 Fog : 383 scattered clouds: 536
Max. :1043 Max. :100.00 Max. :14.00 Max. :360.0 Max. :9.865 Max. :100.00 Max. :804.0 (Other): 536 (Other) :1830
NA's :37 NA's :37 NA's :37 NA's :37 NA's :9066 NA's :37 NA's :37 NA's : 37 NA's : 37
weather_icon temp.c temp.c.group weekday hours chb.all chb.background chb.traffic cht.all
01n :1593 Min. :-10.02 Min. :-10.00 sun :1331 4 : 392 Min. :0.2000 Min. :0.1000 Min. :0.200 Min. : 0.000
04d :1390 1st Qu.: 4.00 1st Qu.: 5.00 wed :1316 6 : 392 1st Qu.:0.6667 1st Qu.:0.5000 1st Qu.:0.750 1st Qu.: 1.333
01d :1379 Median : 10.00 Median : 11.00 fri :1314 1 : 391 Median :0.9333 Median :0.7000 Median :1.050 Median : 2.000
04n :1171 Mean : 10.21 Mean : 10.24 thu :1303 3 : 390 Mean :1.1730 Mean :0.9167 Mean :1.303 Mean : 2.483
50n : 673 3rd Qu.: 16.00 3rd Qu.: 17.00 tue :1290 2 : 389 3rd Qu.:1.3667 3rd Qu.:1.1000 3rd Qu.:1.550 3rd Qu.: 3.000
(Other):2913 Max. : 32.00 Max. : 32.00 (Other):2565 (Other):7165 Max. :8.9000 Max. :9.8000 Max. :9.400 Max. :19.667
NA's : 37 NA's :37 NA's :1647 NA's : 37 NA's : 37 NA's :1 NA's :162 NA's :9 NA's :1
cht.background cht.traffic co.all co.traffic no2.all no2.background no2.traffic no2.suburb no.all
Min. : 0.00 Min. : 0.000 Min. :0.1000 Min. :0.1000 Min. : 3.929 Min. : 4.00 Min. : 4.00 Min. : 1.20 Min. : 0.4375
1st Qu.: 1.00 1st Qu.: 1.500 1st Qu.:0.2667 1st Qu.:0.2667 1st Qu.: 19.750 1st Qu.: 15.40 1st Qu.: 31.00 1st Qu.: 6.60 1st Qu.: 6.4375
Median : 1.00 Median : 2.500 Median :0.3500 Median :0.3500 Median : 27.625 Median : 22.40 Median : 44.83 Median :10.80 Median : 13.0000
Mean : 1.81 Mean : 2.819 Mean :0.3867 Mean :0.3867 Mean : 29.279 Mean : 25.35 Mean : 45.88 Mean :12.94 Mean : 17.8855
3rd Qu.: 2.00 3rd Qu.: 3.500 3rd Qu.:0.4500 3rd Qu.:0.4500 3rd Qu.: 36.806 3rd Qu.: 32.60 3rd Qu.: 58.33 3rd Qu.:17.00 3rd Qu.: 23.0000
Max. :24.00 Max. :18.500 Max. :2.2500 Max. :2.2500 Max. :144.062 Max. :171.40 Max. :187.67 Max. :66.40 Max. :273.3125
NA's :161 NA's :4 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1
no.background no.traffic no.suburb nox.all nox.background nox.traffic nox.suburb o3.all
Min. : 0.000 Min. : 1.167 Min. : 0.000 Min. : 6.00 Min. : 5.20 Min. : 7.833 Min. : 1.60 Min. : 0.50
1st Qu.: 1.250 1st Qu.: 15.333 1st Qu.: 0.200 1st Qu.: 30.69 1st Qu.: 18.20 1st Qu.: 55.333 1st Qu.: 7.00 1st Qu.: 24.17
Median : 2.800 Median : 31.143 Median : 0.400 Median : 48.27 Median : 27.00 Median : 94.000 Median : 11.60 Median : 42.50
Mean : 6.473 Mean : 40.297 Mean : 1.783 Mean : 56.59 Mean : 35.22 Mean :107.422 Mean : 15.66 Mean : 44.07
3rd Qu.: 6.000 3rd Qu.: 53.500 3rd Qu.: 1.200 3rd Qu.: 71.00 3rd Qu.: 41.60 3rd Qu.:140.333 3rd Qu.: 19.25 3rd Qu.: 60.67
Max. :361.800 Max. :419.667 Max. :90.000 Max. :561.44 Max. :723.80 Max. :828.500 Max. :189.20 Max. :142.67
NA's :1 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1
o3.background o3.traffic o3.suburb pm10.all pm10.background pm10.traffic pm10.suburb so2.all so2.background
Min. : 0.00 Min. : 1.0 Min. : 0.25 Min. : 4.00 Min. : 3.333 Min. : 5.00 Min. : 3.00 Min. : 0.000 Min. : 0.000
1st Qu.: 20.00 1st Qu.:11.0 1st Qu.: 25.62 1st Qu.: 13.91 1st Qu.: 12.667 1st Qu.: 16.80 1st Qu.: 10.00 1st Qu.: 0.500 1st Qu.: 0.000
Median : 38.50 Median :25.0 Median : 45.00 Median : 18.82 Median : 17.667 Median : 22.40 Median : 14.00 Median : 1.000 Median : 1.000
Mean : 40.46 Mean :24.3 Mean : 45.94 Mean : 22.70 Mean : 21.351 Mean : 26.68 Mean : 17.29 Mean : 1.522 Mean : 1.198
3rd Qu.: 57.50 3rd Qu.:35.0 3rd Qu.: 63.00 3rd Qu.: 27.70 3rd Qu.: 26.333 3rd Qu.: 32.20 3rd Qu.: 21.33 3rd Qu.: 1.500 3rd Qu.: 1.000
Max. :138.00 Max. :72.0 Max. :145.00 Max. :283.73 Max. :211.667 Max. :465.40 Max. :106.33 Max. :356.000 Max. :27.000
NA's :2 NA's :8469 NA's :1 NA's :1 NA's :1 NA's :1 NA's :1 NA's :4 NA's :27
so2.traffic wind.deg.name
Min. : 0.000 Length:9156
1st Qu.: 1.000 Class :character
Median : 1.000 Mode :character
Mean : 1.844
3rd Qu.: 2.000
Max. :699.000
NA's :54 pm10 over the year
pollutant = c("pm10.background", "pm10.traffic", "pm10.suburb")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
plot.pollutant(data, pollutant, month = "01", yMax = 150)
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
chb over the year
pollutant = c("chb.background", "chb.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
CHT over the year
pollutant = c("cht.background", "cht.traffic")
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
CO over the year
# there are only traffic sensors for co
pollutant = c("co.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
5. no2 over the year
pollutant = c("no2.background", "no2.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
6. O3 over the year
pollutant = c("o3.background", "o3.traffic", "o3.suburb")
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
so2 over the year
pollutant = c("so2.background", "so2.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak", yMax = 15)
plot.pollutant(data, pollutant, month = "01", yMax = 15)
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
wind correlation
ggplot(data, aes(wind.deg.name,wind_speed, group = wind.deg.name)) + geom_boxplot() + ylim(0, 15) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,pm10.all, group = wind_speed)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,co.traffic, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,no2.all, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,so2.all, group = wind_speed)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,o3.all, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,chb.all, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,cht.all, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,pm10.all, group = wind.deg.name)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,co.traffic, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,no2.all, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,so2.all, group = wind.deg.name)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,o3.all, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,chb.all, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,cht.all, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
temp correlation
ggplot(data, aes(temp.c.group, pm10.all, group = temp.c.group)) + geom_boxplot() + ylim(0, 15) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, co.traffic, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, no2.all, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, so2.all, group = temp.c.group)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, o3.all, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, chb.all, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, cht.all, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
weather main correlation
ggplot(data, aes(weather_main, pm10.all, group = weather_main)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, co.traffic, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, no2.all, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, so2.all, group = weather_main)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, o3.all, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, chb.all, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, cht.all, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
pressure correlation
ggplot(data, aes(pressure, pm10.all, group = pressure)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, co.traffic, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, no2.all, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, so2.all, group = pressure)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, o3.all, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, chb.all, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, cht.all, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
humidity correlation
ggplot(data, aes(humidity, pm10.all, group = humidity)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, co.traffic, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, no2.all, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, so2.all, group = humidity)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, o3.all, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, chb.all, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, cht.all, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
clouds correlation
ggplot(data, aes(clouds_all, pm10.all, group = clouds_all)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, co.traffic, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, no2.all, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, so2.all, group = clouds_all)) + geom_boxplot() + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, o3.all, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, chb.all, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, cht.all, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
pollutant to pollutant correlations
---
title: "Data Exploration of pollutants plus weather in Berlin"
output:
  html_document:
    df_print: paged
  pdf_document: default
  html_notebook: default
---

```{r setup, echo=FALSE}
source("./dataImport.R")
library(lattice)
require(ggplot2)
require(reshape2)
require(plyr)

plot.pollutant = function(data, pollutants, month="00", day="00", title="", yMax=0) {
  df = data[c("dtPosix", pollutants)]
  df = melt(df, id.vars = 'dtPosix', variable.name = "pollutant", value.name = "value")
  
  if (day == "00" & month != "00")
    df = subset(df, format.Date(dtPosix, "%m")==month)
  else if (day != "00" & month != "00")
    df = subset(df, format.Date(dtPosix, "%m")==month & format.Date(dtPosix, "%d")==day)
  
  plot <- ggplot(df, aes(dtPosix,value)) + geom_point(aes(colour = pollutant))

  if (yMax != 0) {
    plot <- plot + coord_cartesian(ylim = c(0, yMax)) 
  }
  
    plot + ggtitle(title)
}  
```


```{r}
# importing our data
data = data.import()

head(data)
```


## Overview
```{r}
summary(data)
```

## pm10 over the year

```{r}
pollutant = c("pm10.background", "pm10.traffic", "pm10.suburb")

plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
  
plot.pollutant(data, pollutant, month = "01", yMax = 150)
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```


## chb over the year
```{r}
pollutant = c("chb.background", "chb.traffic")

plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
  
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```

## CHT over the year
```{r}
pollutant = c("cht.background", "cht.traffic")
  
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```

## CO over the year
```{r}
# there are only traffic sensors for co
pollutant = c("co.traffic")

plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
  
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```

## 5. no2 over the year
```{r}
pollutant = c("no2.background", "no2.traffic")

plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")
  
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```

## 6. O3 over the year
```{r}
pollutant = c("o3.background", "o3.traffic", "o3.suburb")
  
plot.pollutant(data, pollutant, month = "01")
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```

## so2 over the year
```{r}
pollutant = c("so2.background", "so2.traffic")

plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak", yMax = 15)
  
plot.pollutant(data, pollutant, month = "01", yMax = 15)
plot.pollutant(data, pollutant, month = "02")
plot.pollutant(data, pollutant, month = "03")
plot.pollutant(data, pollutant, month = "04")
plot.pollutant(data, pollutant, month = "05")
plot.pollutant(data, pollutant, month = "06")
plot.pollutant(data, pollutant, month = "07")
plot.pollutant(data, pollutant, month = "08")
plot.pollutant(data, pollutant, month = "09")
plot.pollutant(data, pollutant, month = "10")
plot.pollutant(data, pollutant, month = "11")
plot.pollutant(data, pollutant, month = "12")
```

## wind correlation
```{r}

ggplot(data, aes(wind.deg.name,wind_speed, group = wind.deg.name)) + geom_boxplot() + ylim(0, 15) + stat_summary(fun.y=mean, colour="darkred", geom="point")

ggplot(data, aes(wind_speed,pm10.all, group = wind_speed)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,co.traffic, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,no2.all, group = wind_speed)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,so2.all, group = wind_speed)) + geom_boxplot()  + ylim(0, 5)  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,o3.all, group = wind_speed)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,chb.all, group = wind_speed)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind_speed,cht.all, group = wind_speed)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")

ggplot(data, aes(wind.deg.name,pm10.all, group = wind.deg.name)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,co.traffic, group = wind.deg.name)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,no2.all, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,so2.all, group = wind.deg.name)) + geom_boxplot()  + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,o3.all, group = wind.deg.name)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,chb.all, group = wind.deg.name)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(wind.deg.name,cht.all, group = wind.deg.name)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")




```


## temp correlation
```{r}
ggplot(data, aes(temp.c.group, pm10.all, group = temp.c.group)) + geom_boxplot() + ylim(0, 15) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, co.traffic, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, no2.all, group = temp.c.group)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, so2.all, group = temp.c.group)) + geom_boxplot()  + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, o3.all, group = temp.c.group)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, chb.all, group = temp.c.group)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(temp.c.group, cht.all, group = temp.c.group)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
```


## weather main correlation
```{r}
ggplot(data, aes(weather_main, pm10.all, group = weather_main)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, co.traffic, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, no2.all, group = weather_main)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, so2.all, group = weather_main)) + geom_boxplot()  + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, o3.all, group = weather_main)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, chb.all, group = weather_main)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(weather_main, cht.all, group = weather_main)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
```

## pressure correlation
```{r}
ggplot(data, aes(pressure, pm10.all, group = pressure)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, co.traffic, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, no2.all, group = pressure)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, so2.all, group = pressure)) + geom_boxplot()  + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, o3.all, group = pressure)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, chb.all, group = pressure)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(pressure, cht.all, group = pressure)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
```

## humidity correlation

```{r}
ggplot(data, aes(humidity, pm10.all, group = humidity)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, co.traffic, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, no2.all, group = humidity)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, so2.all, group = humidity)) + geom_boxplot()  + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, o3.all, group = humidity)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, chb.all, group = humidity)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(humidity, cht.all, group = humidity)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
```

## clouds correlation

```{r}
ggplot(data, aes(clouds_all, pm10.all, group = clouds_all)) + geom_boxplot() + ylim(0, 70) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, co.traffic, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, no2.all, group = clouds_all)) + geom_boxplot() + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, so2.all, group = clouds_all)) + geom_boxplot()  + ylim(0, 5) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, o3.all, group = clouds_all)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, chb.all, group = clouds_all)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(clouds_all, cht.all, group = clouds_all)) + geom_boxplot()  + stat_summary(fun.y=mean, colour="darkred", geom="point")
```

## pollutant to pollutant correlations

```{r}
ggplot(data, aes(co.traffic, pm10.all, group = co.traffic)) + geom_boxplot() + ylim(0, 100) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(no2.all, pm10.all, group = no2.all)) + geom_boxplot() + ylim(0, 100) + xlim(0, 100) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(so2.all, pm10.all, group = so2.all)) + geom_boxplot() + ylim(0, 100) + xlim(0, 15) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(o3.all, pm10.all, group = o3.all)) + geom_boxplot() + ylim(0, 100)  + stat_summary(fun.y=mean, colour="darkred", geom="point")


ggplot(data, aes(no2.all, co.traffic, group = no2.all)) + geom_boxplot() + xlim(0, 100) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(so2.all, co.traffic, group = so2.all)) + geom_boxplot() + xlim(0, 15) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(o3.all, co.traffic, group = o3.all)) + geom_boxplot() + ylim(0, 1.5)  + stat_summary(fun.y=mean, colour="darkred", geom="point")

ggplot(data, aes(so2.all, no2.all, group = so2.all)) + geom_boxplot() + xlim(0, 30) + stat_summary(fun.y=mean, colour="darkred", geom="point")
ggplot(data, aes(o3.all, no2.all, group = o3.all)) + geom_boxplot()   + stat_summary(fun.y=mean, colour="darkred", geom="point")

ggplot(data, aes(o3.all, so2.all, group = o3.all)) + geom_boxplot() + ylim(0, 20)  + stat_summary(fun.y=mean, colour="darkred", geom="point")
```

